This invention is directed to a method for improving a diagnostic image, such as obtained by, for example, magnetic resonance imaging (MRI) or computed tomography (CT).
Research in computer-aided diagnosis (CAD) has been facilitated by the rapid evolution in computer and medical imaging technologies in recent years. Three-dimensional diagnostic imaging tools, such as magnetic resonance imaging (MRI) and computed tomography (CT), are widely used for a vast range of applications. The result is a large increase in the amount of medical imaging data that must be analyzed. Manual analysis is often labor intensive and error prone.
Automated detection of lung nodules in thoracic CT scans is, for example, an important clinical challenge. Manual analysis by a radiologist is generally time consuming, and may result in missed nodules. Furthermore, the amount of image data that has to be analyzed continues to increase. Blood vessel segmentation in volumetric image data of lungs is a necessary prerequisite in various medical imaging applications. In the context of automated lung nodule detection in thoracic CT scans, segmented blood vessels can be used to resolve local ambiguities based on global considerations, and so improve the performance of existing detection algorithms. Thus, while blood vessels and nodules may share similar characteristics locally, global constraints inherent in the data such as the continuity of blood vessels may be used to discriminate between them. Preliminary results have shown that by using extracted blood vessels it is possible to eliminate approximately 38% of the false positives generated by an existing automated nodule detection system.
Due to its clinical importance, the problem of automated lung nodule detection in thoracic CT scans has attracted multiple research efforts in recent years. Automated nodule detection requires three main processing steps: segmentation and nodule candidate selection, nodule feature extraction, and classification. So far, relatively little effort has been devoted to the incorporation of the structure of blood vessels into the detection of nodules. Blood vessels in the lungs have a tree structure branching from the center toward the periphery of the lung. In addition to branching, blood vessels become smaller toward the periphery of the lung and may become disconnected in the image data produced by the CT scanner. Consequently, such small and disconnected segments of blood vessels are often classified erroneously as nodules. The global structure of a reconstructed tree of blood vessels can impose constraints of continuity and collinearity to reduce the number of nodule candidates, thus improving the classification results of existing systems.
Segmenting the image data correctly to distinguish between the tissue, nodules, and vessels is generally a difficult problem that has direct consequences for subsequent processing steps. Incorrect segmentation can divide structures that should be connected or connect structures that should be separated, thus leading to incorrect interpretation of the data. Due to a generally low contrast between lung tissue and small blood vessels, a common operation that is applied prior to segmentation is the enhancement of blood vessels. Enhancement filters are based on the assumption that blood vessels conform to a tubular model whereas nodules conform to a spherical model. The bifurcation of blood vessels results in junction structures which are indicated by clusters of bright pixels. While vessels, vessel junctions, and nodules, all have a relatively high intensity compared with their neighborhood, the structural assumptions of the models can be used to distinguish among these structures to enhance the contrast of blood vessels and junctions while suppressing nodules and other noise.
Available vessel enhancement filters are typically based on the observation that the ratio between the minimum principal curvature and the maximum principal curvature should be low for vessels (cylinders) and high for nodules (spheres). The principal curvatures are normally obtained as the eigenvalues of the Hessian matrix of the intensity function. The estimation of the Hessian of the intensity function involves second-order partial derivatives and so is highly sensitive to noise. Consequently, smoothing of the data at multiple scales is required. Due to noise and smoothing, junctions are characterized by a high ratio of eigenvalues and so tend to be suppressed by vessel enhancement filters; this, in turn, leads to discontinuity of blood vessels.
There is a need for improved automated analysis of diagnostic images. There is a need for improved enhancing filters to allow improved detection of nodules or other diseased tissue in diagnostic images.